Local AI Advisor

Can Mac Studio M1 Ultra 128 GB run Qwen3 30B-A3B Instruct 2507?

128 GB unified · 800 GB/s memory bandwidth · Qwen3 30B-A3B Instruct 2507 has 30.5B parameters (3.3B active per token).

Short answer

Yes, excellent for chat and comfortable or better for every workload we test.

Best quantization for chat: 6-bit (23 GB of weights) on LM Studio. Generation: 90–120 tok/s, leaving about 93 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUIExcellent6-bit90–120 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellent6-bit80–110 tok/s≈3 s16KDetails
Coding in a repositorywith AiderComfortable8-bit50–70 tok/s≈9 s32KDetails
Agentic codingwith OpenCodeComfortable8-bit50–65 tok/s≈10 s32KDetails
Long documents (64K)with Open WebUIExcellent8-bit30–40 tok/s≈45 s64KDetails

Speeds are estimates calibrated against measured benchmarks on similar hardware. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the 8-bit build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF:Q8_0

Downloads 32.48 GB (Q8_0). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.